Heart Rate Signals Research Articles

Metabolic Communication by SGLT2 Inhibition.

SGLT2 (sodium-glucose cotransporter 2) inhibitors (SGLT2i) can protect the kidneys and heart, but the underlying mechanism remains poorly understood. To gain insights on primary effects of SGLT2i that are not confounded by pathophysiologic processes or are secondary to improvement by SGLT2i, we performed an in-depth proteomics, phosphoproteomics, and metabolomics analysis by integrating signatures from multiple metabolic organs and body fluids after 1 week of SGLT2i treatment of nondiabetic as well as diabetic mice with early and uncomplicated hyperglycemia. Kidneys of nondiabetic mice reacted most strongly to SGLT2i in terms of proteomic reconfiguration, including evidence for less early proximal tubule glucotoxicity and a broad downregulation of the apical uptake transport machinery (including sodium, glucose, urate, purine bases, and amino acids), supported by mouse and human SGLT2 interactome studies. SGLT2i affected heart and liver signaling, but more reactive organs included the white adipose tissue, showing more lipolysis, and, particularly, the gut microbiome, with a lower relative abundance of bacteria taxa capable of fermenting phenylalanine and tryptophan to cardiovascular uremic toxins, resulting in lower plasma levels of these compounds (including p-cresol sulfate). SGLT2i was detectable in murine stool samples and its addition to human stool microbiota fermentation recapitulated some murine microbiome findings, suggesting direct inhibition of fermentation of aromatic amino acids and tryptophan. In mice lacking SGLT2 and in patients with decompensated heart failure or diabetes, the SGLT2i likewise reduced circulating p-cresol sulfate, and p-cresol impaired contractility and rhythm in human induced pluripotent stem cell-derived engineered heart tissue. SGLT2i reduced microbiome formation of uremic toxins such as p-cresol sulfate and thereby their body exposure and need for renal detoxification, which, combined with direct kidney effects of SGLT2i, including less proximal tubule glucotoxicity and a broad downregulation of apical transporters (including sodium, amino acid, and urate uptake), provides a metabolic foundation for kidney and cardiovascular protection.

A lightweight hybrid CNN-LSTM explainable model for ECG-based arrhythmia detection

Objective:Electrocardiogram (ECG) is the most frequent and routine diagnostic tool used for monitoring heart electrical signals and evaluating its functionality. The human heart can suffer from a variety of diseases, including cardiac arrhythmias. Arrhythmia is an irregular heart rhythm that in severe cases can lead to stroke and can be diagnosed via ECG recordings. Since early detection of cardiac arrhythmias is of great importance, computerized and automated classification and identification of these abnormal heart signals have received much attention for the past decades. Methods:This paper introduces a light Deep Learning (DL) approach for high accuracy detection of 8 different cardiac arrhythmias and normal rhythms. To employ DL techniques, the ECG signals were preprocessed using resampling and baseline wander removal techniques. The classification was performed using an 11-layer network employing a combination of Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM). Results:In order to evaluate the proposed technique, ECG signals are chosen from the two physionet databases, the MIT-BIH arrhythmia database and the long-term AF database. The proposed DL framework based on the combination of CNN and LSTM showed promising results than most of the state-of-the-art methods. The proposed method reaches the mean diagnostic accuracy of 98.24%. Conclusion:A trained model for arrhythmia classification using diverse ECG signals were successfully developed and tested. Significance:This study presents a lightweight classification technique with high diagnostic accuracy compared to other notable methods, making it a potential candidate for implementation in Holter monitor devices for arrhythmia detection. Finally, we used SHapley Additive exPlanations (SHAP), the most popular Explainable Artificial Intelligence (XAI) method to understand how our model make predictions. The results indicate that those features (ECG samples) that have contributed the most to a prediction are consonant with clinicians’ decisions. Therefore, the use of interpretable models increases the trust of clinicians in AI and thus leads to decreasing the number of misdiagnoses of cardiovascular diseases.

DATA ANALYTICS FOR HEALTH-RELEVANT EVENTS DETECTION BASED UPON LONGITUDINAL FITBIT HEART RATE DATA

Abstract The increasing prevalence of wearable devices enables low-cost, long-term collection of health relevant data such as heart rate, exercise, and sleep signals. Currently these data are used to monitor short term changes with limited interpretation of their relevance to health. These data provide an untapped resource to monitor daily and long-term activity patterns. Changes and trends identified from such data can provide insights and guidance to the management of many chronic conditions that change over time. In this study we conducted a machine learning based analysis of longitudinal heart rate data collected over multiple years from Fitbit devices. We built a multi-resolutional pipeline for time series analysis, using model-free clustering methods inspired by statistical conformal prediction framework. With this method, we were able to detect health relevant events, their interesting patterns (e.g., daily routines, seasonal differences, and anomalies), and correlations to acute and chronic changes in health conditions. We present the results, lessons, and insights learned, and how to address the challenge of lack of labels. The study confirms the value of long-term heart rate data for health monitoring and surveillance, as complementary to extensive yet intermittent examinations by health care providers.

A novel method for distinction heart rate variability during meditation using LSTM recurrent neural networks based on visibility graph

Detection of dynamical complexity variations in heart rate signals during meditation practice can be considered as an important criterion for further understanding cardiovascular functions. This paper introduces a computational framework for categorizing the heart rate variability (HRV) differences of subjects during meditation compared to before meditation using information from heart rate signals. Heart rate data are obtained from the Physionet database. Because of the dynamic and nonlinear nature of heart rate signals, visibility graph features were utilized for analysis. The recurrent neural network (RNN) based on long short-term memory (LSTM) was used to classify subjects into two different states before and during meditation relying on its performance in terms of the VG-based features. The results show that the proposed approach managed to categorize the heart rate signals into two groups: before and during meditation, with a high accuracy of 99.25%. Moreover, the proposed approach enjoys the potential to extremely ease the adoption of the running situation of the cardiac system and physiological functions by disclosing remarkable differences between the HRV reactions of the two mentioned states.

Thrombosis in the Coronary Microvasculature Impairs Cardiac Relaxation and Induces Diastolic Dysfunction.

Heart failure with preserved ejection fraction is proposed to be caused by endothelial dysfunction in cardiac microvessels. Our goal was to identify molecular and cellular mechanisms underlying the development of cardiac microvessel disease and diastolic dysfunction in the setting of type 2 diabetes. We used Leprdb/db (leptin receptor-deficient) female mice as a model of type 2 diabetes and heart failure with preserved ejection fraction and identified Hhipl1 (hedgehog interacting protein-like 1), which encodes for a decoy receptor for HH (hedgehog) ligands as a gene upregulated in the cardiac vascular fraction of diseased mice. We then used Dhh (desert HH)-deficient mice to investigate the functional consequences of impaired HH signaling in the adult heart. We found that Dhh-deficient mice displayed increased end-diastolic pressure while left ventricular ejection fraction was comparable to that of control mice. This phenotype was associated with a reduced exercise tolerance in the treadmill test, suggesting that Dhh-deficient mice do present heart failure. At molecular and cellular levels, impaired cardiac relaxation in DhhECKO mice was associated with a significantly decreased PLN (phospholamban) phosphorylation on Thr17 (threonine 17) and an alteration of sarcomeric shortening ex vivo. Besides, as expected, Dhh-deficient mice exhibited phenotypic changes in their cardiac microvessels including a prominent prothrombotic phenotype. Importantly, aspirin therapy prevented the occurrence of both diastolic dysfunction and exercise intolerance in these mice. To confirm the critical role of thrombosis in the pathophysiology of diastolic dysfunction, we verified Leprdb/db also displays increased cardiac microvessel thrombosis. Moreover, consistently, with Dhh-deficient mice, we found that aspirin treatment decreased end-diastolic pressure and improved exercise tolerance in Leprdb/db mice. Altogether, these results demonstrate that microvessel thrombosis may participate in the pathophysiology of heart failure with preserved ejection fraction.

IoT-based Heart Signal Processing System for Driver Drowsiness Detection

Traffic accidents often result in loss of life and significant economic losses. Indonesia's high number of traffic accidents indicates the need for effective solutions to overcome this problem. Developing a drowsiness detection device is one effort that can be made to reduce accidents caused by drowsy drivers. The data obtained in this study used driver heart rate data. The drowsiness detection tool was developed using the Wemos D1 Pro Esp8266 microcontroller and MAX30102 sensor. Testing was carried out on 25 subjects under two conditions: 'Drowsy' and 'Normal.' The driver's level of drowsiness is determined based on the heart rate measured by the detection device. The Blynk application is used as a visual interface to provide notifications via smartphone if the driver is drowsy. The accuracy of the drowsiness detection tool was compared with the results obtained from the Pulse Oximeter. This research shows that the drowsiness detection tool using the Wemos D1 Pro Esp8266 microcontroller and MAX30102 sensor has an accuracy of around 98% when compared with the pulse oximeter. The Blynk application successfully sends notifications precisely when the driver is drowsy. This study highlights the potential of drowsiness detection devices to improve traffic safety and reduce accidents caused by drowsy drivers.

Healthy Young POLes – HYPOL database with synchronised beat-to-beat heart rate and blood pressure signals

Data sharing in medical research entails making research data available to other researchers for review, re-use, and collaboration purposes. This paper seeks to describe the HYPOL (Healthy Young POLes) database, which has been prepared for sharing. This database houses the clinical characteristics and beat-to-beat cardiovascular time series of 278 individuals of Polish descent, all aged between 19 and 30 years. The data were collected from healthy volunteers who participated in multiple projects carried out at the Department of Cardiology-Intensive Therapy research laboratory, Poznan University of Medical Sciences, Poznan, Poland. The cardiovascular time series data was obtained from non-invasive continuous finger blood pressure and ECG recordings, with sessions lasting up to 45 minutes. The HYPOL database includes an xls file detailing the main clinical characteristics and text files that capture ECG-derived RR intervals, finger systolic, diastolic, and mean blood pressure values, as well as the duration of interbeat intervals. There are data from 149 women (53.6% of the total) and 129 men. The median age of all participants studied is 24 years, their BMI was <24 kg/m2, pulse rate and blood pressure were normal. The median duration of the recordings was almost 30 minutes. In addition, we summarise selected parameters of heart rate variability (HRV) and heart rate asymmetry (HRA). The HYPOL database is available at hypol.ump.edu.pl. The download of data is free of charge after simple registration. Researchers and engineers can use the database for their purposes, except for selling it. The data can be used for testing various mathematical algorithms for HRV, HRA, blood pressure variability and asymmetry, and baroflex function.

Thrombosis in the coronary microvasculature may be responsible for impaired cardiac relaxation and diastolic dysfunction

Abstract Introduction Heart Failure with Preserved Ejection Fraction (HFpEF) is proposed to be caused by endothelial dysfunction in cardiac small vessels. We previously identified Hhipl1,as a gene upregulated in the coronary vasculature of Leptin receptor deficient mice (Leprdb/db) a well-established mouse model of HFpEF. Importantly, Hhipl1 encodes for a decoy receptor of Desert Hedgehog (DHH) which is known to be critical for endothelial integrity. Objective Our objective is to investigate the functional consequences of impaired Hedgehog (HH) signaling in the adult heart in order to identify novel mechanisms underlying the development of diastolic dysfunction. Method To do so, Cdh5-Cre/ERT2, DhhFlox/Flox (DhhECKO) mice and their control littermates were administered with tamoxifen at 8 weeks of age to induce Dhh KO. Their cardiac function, exercise tolerance, and the phenotype of their coronary vasculature were assessed one month later. Results DhhECKO mice presented significantly reduced exercise tolerance, increased end diastolic pressure (EDP) and Tau, with no change in their ejection fraction consistent with diastolic dysfunction. At molecular and cellular level, impaired cardiac relaxation in DhhECKO mice was associated with a significantly decreased phospholamban phosphorylation on Thr17 and an alteration of sarcomeric shortening in ex-vivo. Besides, as expected, DhhECKO mice exhibited phenotypic changes in their coronary vasculature including a prominent pro-thrombotic phenotype (63±6,2 vs 25±5,2 thrombi/mm²; p<0,001) leading to an impaired capillary perfusion and local hypoxia. Notably, antiaggregant therapies (aspirin and clopidogrel) prevented the occurrence of both diastolic dysfunction and exercise intolerance in DhhECKO mice demonstrating for the first time that thrombosis may promote diastolic dysfunction. Importantly, we confirmed the critical role of thrombosis in Leprdb/db mice which also displayed increased cardiac small vessel thrombosis in comparison to control mice. Alike DhhECKO mice, we found that antiaggregants decreased EDP (6,3±0,4 mmHg in aspirin-treated vs 11,3 ± 0,79 in control mice; p=0,001) and improved exercise tolerance in Leprdb/db mice (34 ± 2,52 min in aspirin-treated vs 24 ± 3,55 in control mice; p=0,004). Conclusion Altogether, these results demonstrate that small vessel thrombosis may participate in the pathophysiology of heart failure with preserved ejection fraction.

Factors for incorrect automated atrial fibrillation identification in large-scale screening

Abstract Introduction Correct heart rhythm diagnosis in single-lead electrocardiogram (ECG) screening for atrial fibrillation (AF) is paramount for large-scale implementation. Aim Examine the accuracy of automated AF detection and identify patient factors and ECG signal quality leading to incorrect measurements based on human overread and a machine learning approach. Methods Screening for AF using a single-lead ECG device (MyDiagnostick, Biomedical Systems, Netherlands) was performed on 7295 customers in community pharmacies in Germany. ECG rhythm strips were blinded for automatic heart rhythm analysis and were analyzed by human overread for heart rhythm and signal quality from excellent to uninterpretable (Figure 1A). A machine learning-based estimation was used to score signal quality (MLSQE). Inclusion criteria were 30 seconds of consecutive ECG recording and complete data of pharmacy visit. In total, 6732 participants were eligible for analysis. Results An incorrect automated heart rhythm diagnosis was found for 206 subjects (false positive: 112; false negative: 94). By human overread 80% (N=5377) of the recorded ECGs were rated with excellent and good signal quality in which heart rhythm diagnosis based on visible atrial electrical activity (Figure 1A). The proportion of incorrect automated rhythm diagnosis was higher in ECGs with poorer signal quality. A regression analysis revealed patient characteristics prone to incorrect heart rhythm diagnosis included male sex (OR 1.61 95% confidence interval (CI) 1.22-2.13, P<0.001), age (per year OR 1.06 95%CI 1.04-1.09, P<0.001), body height (per cm OR 1.02 95%CI 1.00-1.03, P=0.03), and body weight (per kg OR 0.99 95%CI 0.98-0.99, P=0.005). A multivariate model for prediction of correct heart rhythm diagnosis showed a modest area under the curve (AUC) of 0.65 (sensitivity 61%, specificity 58%; Figure 1B). The prediction improved by adding information on signal quality rated by human overread (AUC 0.77, sensitivity 77%, specificity 62%) and even more by the machine-learning based MLSQE score (AUC 0.87, sensitivity 71%, specificity 90%). Conclusion The patient characteristics prone to incorrect automated heart rhythm diagnosis of male sex, older age, greater height, and lower body weight probably reflect the target population of many AF screening cohorts. Automated MLSQE scoring for ECG signal quality could improve accuracy in clinical use and potentially reduces the need for cumbersome and time-consuming human overread.Figure 1A - Signal quality by human rating and proportion of incorrect automated heart rhythm diagnosis. B - Prediction of correct heart rhythm diagnosis. Blue line - Multivariate model with forward selection considering age, height, weight, body mass index, congestive heart failure, hypertension, diabetes mellitus, vascular disease, stroke/TIA and sex. Purple line - Model with human signal quality (SQ) score. Green line – Model with MLSQE score.

Abstract 17705: BAG5 Plays a Critical Role in the Regulation of Cellular Oxidative Stress and Heart Function

Introduction: Molecular chaperone and co-chaperone play important roles in maintaining cardiac function by regulating cellular proteostasis and reducing oxidative stress during hypertrophy. Bcl2 binding proteinBAG5 belongs to Bcl-2 associated anthanogene (BAG) family and acts as a co-chaperone to HSP70. Genetic mutation of BAG5 induces dilated cardiomyopathy and arrhythmia. However, the molecular mechanism of BAG5 in heart function is unclear. In this study, we explore the regulatory role of BAG5 in heart function. Hypothesis: We hypothesized that BAG5 is an important Co-chaperone of cardiomyocytes and regulates heart function through the regulation of cellular proteostasis and oxidative stress. Method: We have generated adenovirus to overexpress and knockdown the BAG5 protein in cardiomyocytes. Additionally, we have generated BAG5 knockout mice to test the effect of BAG5 deficiency on cardiac function. Further, we have conducted a proteomics study to explore the role of BAG5 in heart function. Additionally, we tested the role of BAG5 in isoproterenol-induced cardiac hypertrophy. Results: Our results show that deficiency of BAG5 induces cellular hypertrophy and induction of cellular autophagy. Cardiac function analysis by echocardiography shows that the knockdown of BAG5 compromises heart function. Further, a protein interaction study shows that BAG5 participates in several critical heart signaling, including mitochondrial oxphos and glutathione-mediated regulation of oxidative stress. Additionally, we found that the knockdown of BAG5 worsens heart function during isoproterenol treatment. Conclusion: Our study shows a novel function of BAG5 and BAG5 can be exploited in treating cardiac hypertrophy and reducing oxidative stress.

Multi-omics analysis of zebrafish response to tick saliva reveals biological processes associated with alpha-Gal syndrome

The alpha-Gal syndrome (AGS) is a tick-borne allergy. A multi-omics approach was used to determine the effect of tick saliva and mammalian meat consumption on zebrafish gut transcriptome and proteome. Bioinformatics analysis using R software was focused on significant biological and metabolic pathway changes associated with AGS. Ortholog mapping identified highly concordant human ortholog genes for the detection of disease-enriched pathways. Tick saliva treatment increased zebrafish mortality, incidence of hemorrhagic type allergic reactions and changes in behavior and feeding patterns. Transcriptomics analysis showed downregulation of biological and metabolic pathways correlated with anti-alpha-Gal IgE and allergic reactions to tick saliva affecting blood circulation, cardiac and vascular smooth muscle contraction, behavior and sensory perception. Disease enrichment analysis revealed downregulated orthologous genes associated with human disorders affecting nervous, musculoskeletal, and cardiovascular systems. Proteomics analysis revealed suppression of pathways associated with immune system production of reactive oxygen species and cardiac muscle contraction. Underrepresented proteins were mainly linked to nervous and metabolic human disorders. Multi-omics data revealed inhibition of pathways associated with adrenergic signaling in cardiomyocytes, and heart and muscle contraction. Results identify tick saliva-related biological pathways supporting multisystemic organ involvement and linking α-Gal sensitization with other illnesses for the identification of potential disease biomarkers.

Distracted driving recognition based on functional connectivity analysis between physiological signals and perinasal perspiration index

Automatic detection of distracted driving is essential to ensure safety of drivers. In this paper, a novel set of features were extracted from thermal and physiological signals in order to detect and recognize distraction of drivers. Thermal video data which measured the temperature of different areas of the face, heart rate, breathing rate and behavioural signals were used while various types of distractions including cognitive, emotional and sensory-motor were applied to the subjects. The proposed discriminator features were extracted by different functional connectivity methods between the perinasal perspiration extracted from thermal images of the face and physiological variables of heart rate and breathing rate. After feature extraction, binary classification methods were applied to detect the distractions. The results showed that using functional connectivity features significantly increased the accuracy of distraction detection system (up to 99.16% with a highly acceptable F1 score of 0.99). Hence, the proposed model significantly improved (p-value<0.001) the detection of driver’s distraction compared to previous studies. Functional connectivity between perinasal perspiration and breathing signals was the most informative feature for distraction detection purpose. This feature set explained almost all modulations in physiological changes by distractions. For the first time, we designed a system capable of recognizing types of distractions including sensory-motor, cognitive, emotional distractions and also normal state. We used the same feature set to recognize different types of distractions by using three-class and four-class classifiers. The suggested methods distinguished three types of distractions with the best accuracy of 81.94% related to cognitive, sensory-motor distraction and no-distraction states. We also tried to discriminate two types of cognitive distractions, analytic and mathematical distractions. The recognition system classified two types of cognitive distractions with an accuracy of 91.78%. The results suggest that there is important and complementary information in the connectivity between facial temperature signals and physiological variables for distraction detection and recognition.

Chrysin restores the cardioprotective effect of ischemic preconditioning in diabetes-challenged rat heart

BackgroundIschemic preconditioning (IPC) is the utmost capable design to achieve protection over ischemia-reperfusion injury (I/R), but this phenomenon gets attenuated during various pathological conditions like diabetes. Chrysin exhibits cardioprotection in various experiments however, its therapeutic potential on IPC-mediated cardioprotection via PI3K-Akt-eNOS pathway in streptozotocin (STZ) triggered diabetes-challenged rat heart is yet to be assessed. For that reason, the experiment has been planned to investigate chrysin's effect on the cardioprotective action of IPC involving the PI3K-Akt-eNOS cascade in rat hearts challenged to diabetes. MethodsThe project was accomplished through means of absorbance studies for biochemical parameters, infarct size measurement (TTC stain) and coronary flow. ResultsThe findings of the present study revealed that STZ drastically augmented the serum glucose level and the chrysin significantly reversed the IPC-stimulated increased coronary flow, nitrite release, and reduced LDH (lactate dehydrogenase), CK-MB (creatine kinase) activities as well as infarct size in diabetes-induced rat heart. Furthermore, chrysin also reversed the IPC-induced reduction in oxidative stress in an isolated Langendorff's perfused diabetic rat heart. Moreover, four episodes of preconditioning by either PI3K or eNOS inhibitor in chrysin-pretreated diabetic rat hearts significantly abolished the protective effect of chrysin. ConclusionConsequently, these observations suggested that chrysin increases the therapeutic efficiency of IPC in mitigating I/R injury via PI3K-Akt-eNOS signalling in diabetes-challenged rat hearts. Hence, chrysin could be a potential alternative option to IPC in diabetic rat hearts.

Accounting for Nulliparity in the Prediction of Hypoxic-Ischemic Encephalopathy Using Cardiotocography.

Nulliparous pregnancies, those where the mother has not previously given birth, are associated with longer labors and hence expose the fetus to more contractions and other adverse intrapartum conditions such as chorioamnionitis. The objective of the present study was to test if accounting for nulliparity could improve the detection of fetuses at increased risk of developing hypoxic-ischemic encephalopathy (HIE). During labor, clinicians assess the fetal heart rate and uterine pressure signals to identify fetuses at risk of developing HIE. In this study, we performed random forest classification using fetal heart rate and uterine pressure features from 40,831 births, including 374 that developed HIE. We analyzed a two-path classification approach that analyzed separately the fetuses from nulliparous and multiparous mothers, and a one-path classification approach that included the clinical variable for nulliparity as a classification feature. We compared these two approaches to a one-path classifier that had no information about the parity of the mothers. We also compared our results to the rate of Caesarean deliveries in each group, which is used clinically to interrupt the progression towards HIE. All the classifiers detected more fetuses that developed HIE than the observed Caesarean rate, but accounting for nulliparity did not improve performance.

Using beat-to-beat heart signals for age-independent biometric verification

Use of non-stationary physiological signals for biometric verification, reduces the ability to forge. Such signals should be simple to acquire with inexpensive equipment. The beat-to-beat information embedded within the time intervals between consecutive heart beats is a non-stationary physiological signal; its potential for biometric verification has not been studied. This work introduces a biometric verification method termed “CompaRR”. Heartbeat was extracted from longitudinal recordings from 30 mice ranging from 6 to 24 months of age (equivalent to ~ 20–75 human years). Fifty heartbeats, which is close to resting human heartbeats in a minute, were sufficient for the verification task, achieving a minimal equal error rate of 0.21. When trained on 6-month-old mice and tested on unseen mice up to 18-months of age (equivalent to ~ 50 human years), no significant change in the verification performance was noted. Finally, when the model was trained on data from drug-treated mice, verification was still possible.

SAT033 Disruption Of GHR In “Middle Aged” Mice Enhances Insulin Sensitivity And Extends Male Lifespan

Abstract Disclosure: S. Duran-Ortiz: None. E.O. List: None. J.A. Young: None. S.C. Mathes: None. R. Basu: None. Y.B. Slama: None. D.E. Berryman: None. Reduced growth hormone (GH) action has been shown to extend healthspan and lifespan in several organisms. In fact, mice and humans with a congenital disruption in the GH receptor (R) gene (GHRKO mice and patients with Laron Syndrome, respectively) have improved insulin sensitivity and are resistant to diabetes, cancer and age-associated cognitive decline. Importantly, the GHRKO mice hold the record for the longest-lived laboratory mice, with a lifespan one week shy of five years of age. GHRKO mice also show reduced markers of aging such as adipose tissue senescence and mTORC1 signaling in liver, kidney, heart and muscle. However, as GH action is required for normal growth and development, we and others have hypothesized that targeted inhibition of GH action at an adult age could be a potent method to extend healthy lifespan. To test this hypothesis and to elucidate how far in development would disruption of GHR extend healthy lifespan, our laboratory recently reported on two mouse lines with post-natal ablation of the GHR at a pubertal age (1.5 months - 1.5mGHRKO mice) and at mature-adult age (6 months - 6mGHRKO mice). We found that ablation of the GHR gene at both 1.5- and 6-months of age extends maximal and mean lifespan preferentially in females. Furthermore, 6mGHRKO mice showed improved oxidative stress resistance while males had improved insulin sensitivity and reduced cancer incidence. With these promising results, we believe that interventions to extend aging should be effective when administered later in life. In the current study, we disrupted the GHR in middle aged mice (12 months - 12mGHRKO mice) using the Cre-Lox system. We found that 12mGHRKO mice exhibit GH insensitivity (high serum GH and low IGF-1 levels), increased adipose tissue mass, reduced lean mass, minimal impact on body weight and length, improved insulin sensitivity and reduced glucose levels in males as well as decreased adipose tissue fibrosis in females compared to males. Moreover, despite the increased fat mass, motor coordination and strength as measured by rotarod and grip strength tests show no deficit in 12mGHRKO mice compared to the controls. Importantly, the male 12mGHRKO mice showed a preferential and significant lifespan extension compared to control mice. Collectively, our results indicate that not only does post-natal GHR disruption does not affect growth and development, but it also confers several crucial healthspan and lifespan advantages. Acknowledgements: This work is supported by the State of Ohio's Eminent Scholar Program, by NIH grant AG059779, by the American Federation for Aging Research (AFAR) and by the Diabetes Institute and by the AMVETS. Presentation: Saturday, June 17, 2023

Innate immune signaling in hearts and buccal mucosa cells of patients with arrhythmogenic cardiomyopathy.

Nuclear factor κB (NF-κB) signaling in cardiac myocytes causes disease in a mouse model of arrhythmogenic cardiomyopathy (ACM) by mobilizing CCR2-expressing macrophages that promote myocardial injury and arrhythmias. Buccal mucosa cells exhibit pathologic features similar to those seen in cardiac myocytes in patients with ACM. We sought to determine if persistent innate immune signaling via NF-κB occurs in cardiac myocytes in patients with ACM and if this is associated with myocardial infiltration of proinflammatory cells expressing CCR2. We also determined if buccal mucosa cells from young subjects with inherited disease alleles exhibit NF-κB signaling. We analyzed myocardium from ACM patients who died suddenly or required cardiac transplantation. We also analyzed buccal mucosa cells from young subjects with inherited disease alleles. The presence of immunoreactive signal for RelA/p65 in nuclei of cardiac myocytes and buccal cells was used as a reliable indicator of active NF-κB signaling. We also counted myocardial CCR2-expressing cells. RelA/p65 signal was seen in numerous cardiac myocyte nuclei in 34 of 36 cases of ACM but not in 19 age-matched control individuals. Cells expressing CCR2 were increased in patient hearts in numbers directly correlated with the number of cardiac myocytes showing NF-κB signaling. NF-κB signaling was observed in buccal cells in young subjects with active disease. Patients with clinically active ACM exhibit persistent innate immune responses in cardiac myocytes and buccal mucosa cells, reflecting a local and systemic inflammatory process. Such individuals may benefit from anti-inflammatory therapy.

Double-sided asymmetric method for automated fetal heart rate baseline calculation.

The fetal heart rate (FHR) signal is used to assess the well-being of a fetus during labor. Manual interpretation of the FHR is subject to high inter- and intra-observer variability, leading to inconsistent clinical decision-making. The baseline of the FHR signal is crucial for its interpretation. An automated method for baseline determination may reduce interpretation variability. Based on this claim, we present the Auto-Regressed Double-Sided Improved Asymmetric Least Squares (ARDSIAsLS) method as a baseline calculation algorithm designed to imitate expert obstetrician baseline determination. As the FHR signal is prone to a high rate of missing data, a step of gap interpolation in a physiological manner was implemented in the algorithm. The baseline of the interpolated signal was determined using a weighted algorithm of two improved asymmetric least squares smoothing models and an improved symmetric least squares smoothing model. The algorithm was validated against a ground truth determined from annotations of six expert obstetricians. FHR baseline calculation performance of the ARDSIAsLS method yielded a mean absolute error of 2.54bpm, a max absolute error of 5.22bpm, and a root mean square error of 2.89bpm. In a comparison between the algorithm and 11 previously published methods, the algorithm outperformed them all. Notably, the algorithm was non-inferior to expert annotations. Automating the baseline FHR determination process may help reduce practitioner discordance and aid decision-making in the delivery room.

MCHeart: Multi-Channel-Based Heart Signal Processing Scheme for Heart Noise Detection Using Deep Learning.

In this study, we constructed a model to predict abnormal cardiac sounds using a diverse set of auscultation data collected from various auscultation positions. Abnormal heart sounds were identified by extracting features such as peak intervals and noise characteristics during systole and diastole. Instead of using raw signal data, we transformed them into log-mel 2D spectrograms, which were employed as input variables for the CNN model. The advancement of our model involves integrating a deep learning architecture with feature extraction techniques based on existing knowledge of cardiac data. Specifically, we propose a multi-channel-based heart signal processing (MCHeart) scheme, which incorporates our proposed features into the deep learning model. Additionally, we introduce the ReLCNN model by applying residual blocks and MHA mechanisms to the LCNN architecture. By adding murmur features with a smoothing function and training the ReLCNN model, the weighted accuracy of the model increased from 79.6% to 83.6%, showing a performance improvement of approximately 4% point compared to the LCNN baseline model.

Evaluation of Telemedicine systems for ECG analysis: Advances in the design of RF schemes

The monitoring and transmission of biomedical signals, particularly ECG is essential in the post-pandemic era. In this research, an RF transmission with a carrier frequency of 2.45 GHz for ECG is used. A testbed for variable n-QAM schemes is developed with a low noise amplifier characterized in its linear region based on its P1dB, to guarantee a low level of induced non-linearities. The system includes an acquisition stage using the Olimex module and electrodes with an Ag/AgCl type sensor, and an algorithm is developed for detecting peaks in heart signals, heart rate and calculation of sample-based heart rate. The transceiver has total control of the transmitted tones, and a signal demodulation process is carried out in the receiver, one of the main challenges in Telemedicine is to ensure the fidelity of a signal, an EVMRMS of 8.36 is obtained for fifteen OFDM symbol frames. The developed system as a Telemedicine proposal provides versatility for signal acquisition, digitalization, data storage and a multivariable n-QAM scheme, which makes it viable for Telemedicine and classification processes.